{ "_id": "6a1d3f0a1d7bb097a0a4049f", "Package": "OpenImageR", "Type": "Package", "Title": "An Image Processing Toolkit", "Version": "1.3.0", "Date": "2023-07-08", "Authors@R": "c( person(\"Lampros\", \"Mouselimis\", email = \"mouselimislampros@gmail.com\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"https://orcid.org/0000-0002-8024-1546\")), person(\"Sight\", \"Machine\", role = \"cph\", comment = \"findHOGFeatures function of the SimpleCV computer vision platform\"), person(\"Johannes\", \"Buchner\", role = \"cph\", comment = \"average_hash, dhash and phash functions of the ImageHash python library\"), person(\"Mohammad\", \"Haghighat\", email = \"haghighat@ieee.org\", role = \"cph\", comment = \"Gabor Feature Extraction\"), person(\"Radhakrishna\", \"Achanta\", email = \"Radhakrishna.Achanta@epfl.ch\", role = \"cph\", comment = \"Author of the C++ code of the SLIC and SLICO algorithms (for commercial use please contact the author)\"), person(\"Oleh\", \"Onyshchak\", role = \"cph\", comment = \"Author of the Python code of the WarpAffine function\") )", "Maintainer": "Lampros Mouselimis ", "BugReports": "https://github.com/mlampros/OpenImageR/issues", "URL": "https://github.com/mlampros/OpenImageR", "Description": "Incorporates functions for image preprocessing, filtering\nand image recognition. The package takes advantage of\n'RcppArmadillo' to speed up computationally intensive\nfunctions. The histogram of oriented gradients descriptor is a\nmodification of the 'findHOGFeatures' function of the\n'SimpleCV' computer vision platform, the average_hash(),\ndhash() and phash() functions are based on the 'ImageHash'\npython library. The Gabor Feature Extraction functions are\nbased on 'Matlab' code of the paper, \"CloudID: Trustworthy\ncloud-based and cross-enterprise biometric identification\" by\nM. Haghighat, S. Zonouz, M. Abdel-Mottaleb, Expert Systems with\nApplications, vol. 42, no. 21, pp. 7905-7916, 2015,\n. The 'SLIC' and 'SLICO'\nsuperpixel algorithms were explained in detail in (i) \"SLIC\nSuperpixels Compared to State-of-the-art Superpixel Methods\",\nRadhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi,\nPascal Fua, and Sabine Suesstrunk, IEEE Transactions on Pattern\nAnalysis and Machine Intelligence, vol. 34, num. 11, p.\n2274-2282, May 2012, and (ii)\n\"SLIC Superpixels\", Radhakrishna Achanta, Appu Shaji, Kevin\nSmith, Aurelien Lucchi, Pascal Fua, and Sabine Suesstrunk, EPFL\nTechnical Report no. 149300, June 2010.", "License": "GPL-3", "Encoding": "UTF-8", "Copyright": "inst/COPYRIGHTS", "SystemRequirements": "libarmadillo: apt-get install -y libarmadillo-dev\n(deb), libblas: apt-get install -y libblas-dev (deb),\nliblapack: apt-get install -y liblapack-dev (deb),\nlibarpack++2: apt-get install -y libarpack++2-dev (deb),\ngfortran: apt-get install -y gfortran (deb), libjpeg-dev:\napt-get install -y libjpeg-dev (deb), libpng-dev: apt-get\ninstall -y libpng-dev (deb), libfftw3-dev: apt-get install -y\nlibfftw3-dev (deb), libtiff5-dev: apt-get install -y\nlibtiff5-dev (deb)", "RoxygenNote": "7.2.3", "VignetteBuilder": "knitr", "Config/pak/sysreqs": "cmake libfftw3-dev make libjpeg-dev libpng-dev\nlibtiff-dev libuv1-dev zlib1g-dev", "Repository": "https://mlampros.r-universe.dev", "Date/Publication": "2023-07-08 07:38:03 UTC", "RemoteUrl": "https://github.com/mlampros/openimager", "RemoteRef": "HEAD", "RemoteSha": "59b3c72be79fcb38e8ce5cdd4678194fe8452ad6", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-06-01 08:06:52 UTC", "User": "root" }, "Author": "Lampros Mouselimis [aut, cre] (ORCID:\n),\nSight Machine [cph] (findHOGFeatures function of the SimpleCV computer\nvision platform),\nJohannes Buchner [cph] (average_hash, dhash and phash functions of the\nImageHash python library),\nMohammad Haghighat [cph] (Gabor Feature Extraction),\nRadhakrishna Achanta [cph] (Author of the C++ code of the SLIC and\nSLICO algorithms (for commercial use please contact the author)),\nOleh Onyshchak [cph] (Author of the Python code of the WarpAffine\nfunction)", "MD5sum": "1f7fb8a93cf23be476430c18097c988f", "_user": "mlampros", "_type": "src", "_file": "OpenImageR_1.3.0.tar.gz", "_fileid": "e680a9392182f7aa78ae097499a574757ed132bb18cafeadd89af481f8ffec54", "_filesize": 5547792, "_sha256": "e680a9392182f7aa78ae097499a574757ed132bb18cafeadd89af481f8ffec54", "_created": "2026-06-01T08:06:52.000Z", "_published": "2026-06-01T08:12:58.621Z", "_distro": "noble", "_jobs": [ { "job": 78811136680, "time": 162, "config": "linux-devel-arm64", "r": "4.7.0", "check": "OK", "artifact": "7326418890" }, { "job": 78811136551, "time": 165, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "7326419971" }, { "job": 78811136661, "time": 171, "config": "linux-release-arm64", "r": "4.6.0", "check": "OK", "artifact": "7326421448" }, { "job": 78811136638, "time": 193, "config": "linux-release-x86_64", "r": "4.6.0", "check": "OK", 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augmentations of a matrix, data frame, array or a list of 3-dimensional arrays (where the third dimension is equal to 3)", "topics": [ "Augmentation" ] }, { "page": "average_hash", "title": "calculation of the 'average hash' of an image", "topics": [ "average_hash" ] }, { "page": "convolution", "title": "convolution", "topics": [ "convolution" ] }, { "page": "cropImage", "title": "crop an image", "topics": [ "cropImage" ] }, { "page": "dhash", "title": "calculation of the 'dhash' of an image", "topics": [ "dhash" ] }, { "page": "dilationErosion", "title": "Dilation or Erosion of an image", "topics": [ "dilationErosion" ] }, { "page": "down_sample_image", "title": "downsampling an image ( by a factor ) using gaussian blur", "topics": [ "down_sample_image" ] }, { "page": "edge_detection", "title": "edge detection (Frei_chen, LoG, Prewitt, Roberts_cross, Scharr, Sobel)", "topics": [ "edge_detection" ] }, { "page": "flipImage", "title": "flip image horizontally or vertically", "topics": [ "flipImage" ] }, { "page": "GaborFeatureExtract", "title": "Gabor Feature Extraction", "topics": [ "GaborFeatureExtract" ] }, { "page": "gamma_correction", "title": "Gamma correction", "topics": [ "gamma_correction" ] }, { "page": "getAffineTransform", "title": "Get Affine Transform", "topics": [ "getAffineTransform" ] }, { "page": "hash_apply", "title": "calculate the binary or the hexadecimal hash for a matrix, array or a folder of images for the average_hash, phash or dhash functions", "topics": [ "hash_apply" ] }, { "page": "HOG", "title": "calculate the HOG (Histogram of oriented gradients) for an image", "topics": [ "HOG" ] }, { "page": "HOG_apply", "title": "calculate the HOG (Histogram of oriented gradients) for a matrix, array or a folder of images", "topics": [ "HOG_apply" ] }, { "page": "image_thresholding", "title": "image thresholding", "topics": [ "image_thresholding" ] }, { "page": "imageShow", "title": "display an image", "topics": [ "imageShow" ] }, { "page": "invariant_hash", "title": "invariant hashing (caclulation of the hamming or the levenshtein distance when the image is flipped, rotated or cropped)", "topics": [ "invariant_hash" ] }, { "page": "List_2_Array", "title": "convert a list of matrices to an array of matrices", "topics": [ "List_2_Array" ] }, { "page": "load_binary", "title": "loads either 2- or 3-dimensional data (where the third dimension is equal to 3) from a binary file", "topics": [ "load_binary" ] }, { "page": "MinMaxObject", "title": "minimum and maximum values of vector, matrix, data frame or array", "topics": [ "MinMaxObject" ] }, { "page": "norm_matrix_range", "title": "Normalize a matrix to specific range of values", "topics": [ "norm_matrix_range" ] }, { "page": "NormalizeObject", "title": "normalize a vector, matrix or array (in the range between 0 and 1)", "topics": [ "NormalizeObject" ] }, { "page": "padding", "title": "Padding of matrices or n-dimensional arrays with a user specified value", "topics": [ "padding" ] }, { "page": "phash", "title": "calculation of the 'phash' of an image", "topics": [ "phash" ] }, { "page": "readImage", "title": "this function reads various types of images", "topics": [ "readImage" ] }, { "page": "resizeImage", "title": "resize an image using the 'nearest neighbors' or the 'bilinear' method", "topics": [ "resizeImage" ] }, { "page": "rgb_2gray", "title": "convert an RGB image to Gray", "topics": [ "rgb_2gray" ] }, { "page": "RGB_to_HSV", "title": "Conversion of RGB to HSV colour type", "topics": [ "RGB_to_HSV" ] }, { "page": "RGB_to_Lab", "title": "Conversion of RGB to Lab colour type", "topics": [ "RGB_to_Lab" ] }, { "page": "rotateFixed", "title": "Rotate an image by 90, 180, 270 degrees", "topics": [ "rotateFixed" ] }, { "page": "rotateImage", "title": "Rotate an image using the 'nearest' or 'bilinear' method", "topics": [ "rotateImage" ] }, { "page": "superpixel_bbox", "title": "Bounding box for the superpixel labels", "topics": [ "superpixel_bbox" ] 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